Official Development Assistance and Institutional Quality on Undeveloped countries
Oscar Eduardo Morales Cárdenas
2024-08-05
Secciones
Referencias
International aid may take the form of multilateral aid – provided through international bodies such as the UN, or NGOs such as Oxfam – or bilateral aid, which operates on a government-to-government basis. There is considerable debate about whether international aid works, in the sense of reducing poverty and stimulating development.
However, the effectiveness of aid is often diluted by corruption. Aid is invariably channeled through the governments of recipient countries, in which power is often concentrated in the hands of a few politicians and bureaucrats, and the mechanisms of accountability are, at best, poorly developed. This tends to benefit corrupt leaders and elites rather than the people, projects and programs for which it was intended.
Watts, Carl. (2014). Re: Does foreign aid help the developing countries towards development?. Retrieved from: https://www.researchgate.net/post/Does_foreign_aid_help_the_developing_countries_towards_development/5322005ed039b1e7648b459c/citation/download.
The hypothesis that foreign aid can promote growth in developing countries was explored, using panel data series for foreign aid, while accounting for regional differences in Asian, African, Latin American, and the Caribbean countries as well as the differences in income levels, the results of this study also indicate that foreign aid has mixed effects on economic growth in developing countries.
Ekanayake, E. & Chatrna, Dasha. (2010). The effect of foreign aid on economic growth in developing countries. Journal of International Business and Cultural Studies. 3.
This study examines the relationships between foreign aid, institutional structure, and economic performance for 80 countries in Europe, America, Africa, and Asia. It is found that official development assistance and the quality of institutional structure in the sample countries affect economic growth positively.
Hayaloğlu, Pınar. (2023). Foreign Aid, Institutions, and Economic Performance in Developing Countries. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi. 18. 748-765. 10.17153/oguiibf.1277348.
Manual para replicar
Cargando Librerias
Algunas librerias y paquetes usados para obtener y descargar los datos
library(tidyverse) # manejo de dataframes## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.2 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(WDI) # libreria para acceder a metadata de banco mundial
library(readxl) # leer archivos de excel
library(readr) # leer archivos csv
library(visdat) # visualizacion de datos como graficos
library(plotly) # graficos##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## layoutlibrary(purrr) # funcion map
library(plm) # modelos lineales para datos panel##
## Attaching package: 'plm'## The following objects are masked from 'package:dplyr':
##
## between, lag, leadlibrary(car) # test y utilidaddes para modelos## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
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## recode## The following object is masked from 'package:purrr':
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## someObtener datos
Datos para paises bajos ingresos sean utilizados, segun clasificación del banco mundial, hay 26 paises de bajos ingresos y 51 de ingresos medios bajos
country_class <- read_excel("CLASS.xlsx")
country_class %>%
filter(!is.na(Region), !is.na(`Income group`)) %>%
group_by(`Income group`) %>%
summarise(countries = n()) %>%
arrange(factor(`Income group`, levels = c('High income', 'Upper middle income', 'Lower middle income', 'Low income')))Listado de paises a analisar:
my_countries <- country_class %>%
filter(!is.na(Region), `Income group` %in% c('Low income', 'Lower middle income')) %>%
select(Code)
my_countriesHacer la respectiva asociacion de nombres iso3c e iso2c
my_countries$iso2c <- WDI_data$country %>%
filter(iso3c %in% my_countries$Code) %>%
.$iso2c
my_countriesDatos del banco mundial (para ODA y los indices de gobernanza) y el Human Development Reports API son descargados desde scripts de Python. Son almacenados en archivos CSV y luego son cargados aqui:
cargar HDI
datos_HDI <- read_csv("datos_python_HDI.csv", col_names = c('Code', 'iso2c', 'indicator', 'year', 'value'),
col_types = list(col_character(), col_character(), col_character(), col_double(), col_double()))
hdi_indicators <- datos_HDI %>% distinct(indicator) %>% .$indicatorcargar ODA, GDP, POP.GROW
oda_indicators <- c(
'DT_ODA_ALLD_CD',
'DT_ODA_ALLD_KD',
'DT_ODA_OATL_CD',
'DT_ODA_OATL_KD',
'DT_ODA_ODAT_CD',
'DT_ODA_ODAT_GI_ZS',
'DT_ODA_ODAT_GN_ZS',
'DT_ODA_ODAT_KD',
'DT_ODA_ODAT_MP_ZS',
'DT_ODA_ODAT_PC_ZS',
'DT_ODA_ODAT_XP_ZS'
)
gob_indicators <- c(
'CC_EST',
'CC_NO_SRC',
'CC_PER_RNK',
'CC_PER_RNK_LOWER',
'CC_PER_RNK_UPPER',
'CC_STD_ERR',
'GE_EST',
'GE_NO_SRC',
'GE_PER_RNK',
'GE_PER_RNK_LOWER',
'GE_PER_RNK_UPPER',
'GE_STD_ERR',
'PV_EST',
'PV_NO_SRC',
'PV_PER_RNK',
'PV_PER_RNK_LOWER',
'PV_PER_RNK_UPPER',
'PV_STD_ERR',
'RQ_EST',
'RQ_NO_SRC',
'RQ_PER_RNK',
'RQ_PER_RNK_LOWER',
'RQ_PER_RNK_UPPER',
'RQ_STD_ERR',
'RL_EST',
'RL_NO_SRC',
'RL_PER_RNK',
'RL_PER_RNK_LOWER',
'RL_PER_RNK_UPPER',
'RL_STD_ERR',
'VA_EST',
'VA_NO_SRC',
'VA_PER_RNK',
'VA_PER_RNK_LOWER',
'VA_PER_RNK_UPPER',
'VA_STD_ERR'
)
gdp_indicators <- c(
'NY_ADJ_NNTY_PC_CD',
'NY_ADJ_NNTY_PC_KD',
'NY_ADJ_NNTY_PC_KD_ZG',
'NY_GDP_PCAP_CN',
'NY_GDP_PCAP_KN',
'NY_GDP_PCAP_CD',
'NY_GDP_PCAP_KD',
'NY_GDP_MKTP_KD_ZG',
'NY_GDP_DEFL_ZS_AD',
'NY_GDP_DEFL_ZS',
'NY_GDP_MKTP_CD',
'NY_GDP_MKTP_CN',
'NY_GDP_MKTP_KN',
'NY_GDP_MKTP_KD',
'NY_GDP_PCAP_KD_ZG',
'NY_GDP_PCAP_PP_KD',
'NY_GDP_PCAP_PP_CD',
'SL_GDP_PCAP_EM_KD',
'SP_POP_GROW'
)
datos_WB <- data.frame(indicator = character(), iso2c = character(), year = double(), value = double())
suppressWarnings(
for (indicator in c(oda_indicators, gob_indicators, gdp_indicators)) {
datos_WB <- rbind(datos_WB, read_csv(paste("datos_python", indicator, ".csv", sep =''),
col_names = c('indicator', 'iso2c', 'year', 'value'),
col_types = list(col_character(), col_character(), col_double(), col_double())))
}
)cargar POVERTY
Poverty <- read_excel("GlobalExtremePovertyDollaraDay_Compact.xlsx", sheet = "Data Long Format")
names(Poverty) <- c("ccode", "country", "year", "value")
Poverty[Poverty=="Cape Verde"] <- "Cabo Verde"
Poverty[Poverty=="Congo"] <- "Congo, Rep."
Poverty[Poverty=="Egypt"] <- "Egypt, Arab Rep."
Poverty[Poverty=="Iran"] <- "Iran, Islamic Rep."
Poverty[Poverty=="Kyrgyzstan"] <- "Kyrgyz Republic"
Poverty[Poverty=="Laos"] <- "Lao PDR"
Poverty[Poverty=="Macedonia"] <- "North Macedonia"
Poverty[Poverty=="Russia"] <- "Russian Federation"
Poverty[Poverty=="Slovakia"] <- "Slovak Republic"
Poverty[Poverty=="South Korea"] <- "Korea, Rep."
Poverty[Poverty=="Swaziland"] <- "Eswatini"
Poverty[Poverty=="Syria"] <- "Syrian Arab Republic"
Poverty[Poverty=="The Gambia"] <- "Gambia, The"
Poverty[Poverty=="Turkey"] <- "Turkiye"
Poverty[Poverty=="Venezuela"] <- "Venezuela, RB"
Poverty[Poverty=="Yemen"] <- "Yemen, Rep."
Poverty <- Poverty %>%
filter(year > 1994) %>%
merge(WDI_data$country, all.x = TRUE) %>%
mutate(indicator = 'POV') %>%
merge(my_countries) %>%
select(indicator, iso2c, year, value)Manipulacion de Datos
Transformar la estructura de los datos para una mejor comprension
datos_paper <- rbind(datos_WB, datos_HDI %>% select(indicator, iso2c, year, value), Poverty) %>%
pivot_wider(names_from = indicator, values_from = value)Revisar que datos estan como faltantes
ODA
vis_dat(datos_paper %>% select(all_of(gsub("_", ".", oda_indicators)))) 
# DT.ODA.OATL.CD and DT.ODA.OATL.KD faltan
# DT.ODA.ODAT.GI.ZS, DT.ODA.ODAT.GN.ZS, DT.ODA.ODAT.MP.ZS and DT.ODA.ODAT.XP.ZS tienen faltas
# Un par de ocurrencias pais-año que faltan datosGDP
vis_dat(datos_paper %>% select(NY.GDP.PCAP.CN, NY.GDP.PCAP.CD)) 
# NY.GDP.PCAP.CN, NY.GDP.PCAP.CD, NY.GDP.MKTP.CD, NY.GDP.MKTP.CN son buenos candidatos para usar como variables,
# 'SY'falta PIB per Capita en 2022, 2023 sin datos algunos paisesGOV
vis_dat(datos_paper %>% arrange(year) %>% select(all_of(gsub("_", ".", gob_indicators)))) 
# Datos del 2000 para atras tienen espacios faltantes HDI
vis_dat(datos_paper %>% select(all_of(hdi_indicators))) 
# abr, co2_prod, le, le_f, le_m, mmr son las pocas categorias sin datos faltantes
# hdi faltante en multiples ocacionesPOP.GROW
vis_dat(datos_paper %>% arrange(iso2c) %>% select(SP.POP.GROW)) 
# ZW no tiene datos de crecimiento poblacionalPOV
vis_dat(datos_paper %>% arrange(iso2c) %>% select(POV)) 
# Hay muchos paises sin datos2001 - 2022
Tomando en cuenta los datos faltantes, hacer filtros para seleccionar una muestra mas pequeña
vis_dat(datos_paper %>%
filter(!iso2c %in% c('SS', 'ZW', 'BT', 'ER', 'GW', 'KP', 'LB', 'NG', 'PS', 'SO', 'VU', 'FM', 'KI', 'TL', 'CV', 'SB','SY'),
!year %in% c(1995, 1996, 1997, 1998, 1999, 2000, 2023)) %>%
select(iso2c, year, hdi, DT.ODA.ALLD.CD, DT.ODA.ALLD.KD, DT.ODA.ODAT.CD, DT.ODA.ODAT.KD, DT.ODA.ODAT.PC.ZS,
NY.GDP.PCAP.CN, NY.GDP.PCAP.CD, SP.POP.GROW, POV, all_of(gsub("_", ".", gob_indicators)),
))
2002 - 2002
vis_dat(datos_paper %>%
filter(!iso2c %in% c('SS', 'ZW', 'BT', 'ER', 'GW', 'KP', 'LB', 'NG', 'PS', 'SO', 'VU', 'FM', 'KI', 'TL', 'CV', 'SB','SY'),
!year %in% c(1995, 1996, 1997, 1998, 1999, 2000, 2001, 2023)) %>%
select(iso2c, year, hdi, DT.ODA.ALLD.CD, DT.ODA.ALLD.KD, DT.ODA.ODAT.CD, DT.ODA.ODAT.KD, DT.ODA.ODAT.PC.ZS,
NY.GDP.PCAP.CN, NY.GDP.PCAP.CD, SP.POP.GROW, POV, all_of(gsub("_", ".", gob_indicators))
))
Operador Diferencia
De 2232 observaciones reducimos a 1260 (2002 hasta 2022) o a 1320 (2001 hasta 2022)
Aplicar Operador diferencia
datos_model <- datos_paper %>%
filter(!iso2c %in% c('SS', 'ZW', 'BT', 'ER', 'GW', 'KP', 'LB', 'NG', 'PS', 'SO', 'VU', 'FM', 'KI', 'TL', 'CV', 'SB', 'SY'),
!year %in% c(1995, 1996, 1997, 1998, 1999, 2000, 2023)) %>%
select(iso2c, year, hdi, DT.ODA.ALLD.CD, DT.ODA.ALLD.KD, DT.ODA.ODAT.CD, DT.ODA.ODAT.KD, DT.ODA.ODAT.PC.ZS,
NY.GDP.PCAP.CN, NY.GDP.PCAP.CD, SP.POP.GROW, all_of(gsub("_", ".", gob_indicators))
)
datos_model <- datos_model %>% arrange(iso2c, year) %>%
mutate(hdi_diff = hdi - dplyr::lag(hdi),
NY.GDP.PCAP.CD_diff = NY.GDP.PCAP.CD - dplyr::lag(NY.GDP.PCAP.CD),
DT.ODA.ALLD.CD_diff = DT.ODA.ALLD.CD - dplyr::lag(DT.ODA.ALLD.CD),
DT.ODA.ODAT.PC.ZS_diff = DT.ODA.ODAT.PC.ZS - dplyr::lag(DT.ODA.ODAT.PC.ZS)) %>%
filter(!year %in% c(2001))
vis_dat(datos_model)
Promedio de Indices de Gobernanza
datos_model <- datos_model %>%
mutate(GOV = (CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST) / 6)
vis_dat(datos_model)
Visualizacion de datos
Relaciones
Se revisara las relaciones entre las variables graficamente No se ve una relacion clara, hay tanto paises con punteos altos y bajos de GOB que tienen tanto HID altos o bajos Quiza puede verse una leve relacion de mayor punteo en GOB acompañado de mejor punteo den HDI Los datos de GPD si muestran una relacion positiva con el HDI visto en las graficas
HDI
my_plot <- list()
for (col in c('DT.ODA.ALLD.CD', 'DT.ODA.ODAT.PC.ZS', 'DT.ODA.ALLD.CD_diff', 'DT.ODA.ODAT.PC.ZS_diff',
'CC.EST', 'GE.EST', 'PV.EST', 'RQ.EST', 'RL.EST', 'VA.EST')) {
my_plot[[col]] <- plot_ly(x = datos_model[[col]], y = datos_model[['hdi']], type = 'scatter', mode = 'markers', name = col)
}
subplot(my_plot[1:4], nrows = 2, margin = 0.05) %>% layout(title = 'HDI vs ODA')subplot(my_plot[5:10], nrows = 2, margin = 0.05) %>% layout(title = 'HDI vs GOB')HDI diff
my_plot <- list()
for (col in c('DT.ODA.ALLD.CD', 'DT.ODA.ODAT.PC.ZS', 'DT.ODA.ALLD.CD_diff', 'DT.ODA.ODAT.PC.ZS_diff',
'CC.EST', 'GE.EST', 'PV.EST', 'RQ.EST', 'RL.EST', 'VA.EST')) {
my_plot[[col]] <- plot_ly(x = datos_model[[col]], y = datos_model[['hdi_diff']], type = 'scatter', mode = 'markers', name = col)
}
subplot(my_plot[1:4], nrows = 2, margin = 0.05) %>% layout(title = 'HDI diff vs ODA')subplot(my_plot[5:10], nrows = 2, margin = 0.05) %>% layout(title = 'HDI diff vs GOB')GPD.PC
my_plot <- list()
for (col in c('DT.ODA.ALLD.CD', 'DT.ODA.ODAT.PC.ZS', 'DT.ODA.ALLD.CD_diff', 'DT.ODA.ODAT.PC.ZS_diff',
'CC.EST', 'GE.EST', 'PV.EST', 'RQ.EST', 'RL.EST', 'VA.EST')) {
my_plot[[col]] <- plot_ly(x = datos_model[[col]], y = datos_model[['NY.GDP.PCAP.CD']],
type = 'scatter', mode = 'markers', name = col)
}
subplot(my_plot[1:4], nrows = 2, margin = 0.05) %>% layout(title = 'GDP.PC vs ODA')subplot(my_plot[5:10], nrows = 2, margin = 0.05) %>% layout(title = 'GDP.PC vs GOB')GPD.PC diff
my_plot <- list()
for (col in c('DT.ODA.ALLD.CD', 'DT.ODA.ODAT.PC.ZS', 'DT.ODA.ALLD.CD_diff', 'DT.ODA.ODAT.PC.ZS_diff',
'CC.EST', 'GE.EST', 'PV.EST', 'RQ.EST', 'RL.EST', 'VA.EST')) {
my_plot[[col]] <- plot_ly(x = datos_model[[col]], y = datos_model[['NY.GDP.PCAP.CD_diff']], type = 'scatter', mode = 'markers', name = col)
}
subplot(my_plot[1:4], nrows = 2, margin = 0.05) %>% layout(title = 'GDP.PC diff vs ODA')subplot(my_plot[5:10], nrows = 2, margin = 0.05) %>% layout(title = 'GDP.PC diff vs GOB')Visualizacion de Datos historia
Se realizara el mismo proceso con el crecimiento o decrecimiento de HDI anual Viendo la historia de las variables en el tiempo (por pais)
datos_model %>% filter(iso2c == 'AF') %>% plot_ly(x = ~year) %>%
add_trace(y = ~hdi, type = 'scatter', mode = 'lines+markers', name = 'hdi') %>%
add_trace(y = ~NY.GDP.PCAP.CD / 1000, type = 'scatter', mode = 'lines+markers', name = 'gdp.pc') %>%
add_trace(y = ~DT.ODA.ALLD.CD / 10000000000, type = 'scatter', mode = 'lines+markers', name = 'ODA.ALL') %>%
add_trace(y = ~DT.ODA.ODAT.PC.ZS / 1000, type = 'scatter', mode = 'lines+markers', name = 'ODA.PC') %>%
add_trace(y = ~CC.EST, type = 'scatter', mode = 'lines+markers', name = 'CC') %>%
add_trace(y = ~GE.EST, type = 'scatter', mode = 'lines+markers', name = 'GE') %>%
add_trace(y = ~PV.EST, type = 'scatter', mode = 'lines+markers', name = 'PV') %>%
add_trace(y = ~RQ.EST, type = 'scatter', mode = 'lines+markers', name = 'RQ') %>%
add_trace(y = ~RL.EST, type = 'scatter', mode = 'lines+markers', name = 'RL') %>%
add_trace(y = ~VA.EST, type = 'scatter', mode = 'lines+markers', name = 'VA')Modelos Regresion Lineal
Probando modelos sencillos, regresion lineal, Minimos cuadrados, datos panel, HDI o GDP o sus differecias
vd <- c('hdi', 'hdi_diff', 'NY.GDP.PCAP.CD', 'NY.GDP.PCAP.CD_diff')
vi <- c('DT.ODA.ALLD.CD', 'DT.ODA.ALLD.CD_diff', 'DT.ODA.ODAT.PC.ZS', 'DT.ODA.ODAT.PC.ZS_diff')
models <- list()
keys <- character()
for (vd_ in vd) {
for (vi_ in vi) {
key <- paste(vd_, vi_, sep = "~")
keys <- c(keys, key)
#f <- paste(vd_, '~', vi_, '+ CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW')
#f <- paste(vd_, '~', vi_, '+ CC.PER.RNK + GE.PER.RNK + PV.PER.RNK + RQ.PER.RNK + RL.PER.RNK + VA.PER.RNK + SP.POP.GROW')
f <- paste(vd_, '~', vi_, '+ GOV + SP.POP.GROW')
models[[key]] <- lm(f, data=datos_model)
}
}HDI
ODA.ALL
HDI = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[1]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23055 -0.05445 -0.00579 0.05112 0.56380
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.703e-01 6.959e-03 96.33 < 2e-16 ***
## DT.ODA.ALLD.CD 1.115e-11 2.353e-12 4.74 2.38e-06 ***
## GOV 7.339e-02 5.517e-03 13.30 < 2e-16 ***
## SP.POP.GROW -4.422e-02 2.551e-03 -17.34 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08798 on 1256 degrees of freedom
## Multiple R-squared: 0.3091, Adjusted R-squared: 0.3074
## F-statistic: 187.3 on 3 and 1256 DF, p-value: < 2.2e-16# Todas las variables son significativas al 99% excepto Regulatory QualityODA.ALL_diff
HDI = ODA.ALL_diff + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[2]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23315 -0.05477 -0.00606 0.05287 0.57332
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.762e-01 6.907e-03 97.91 <2e-16 ***
## DT.ODA.ALLD.CD_diff 3.402e-13 4.848e-12 0.07 0.944
## GOV 6.966e-02 5.511e-03 12.64 <2e-16 ***
## SP.POP.GROW -4.305e-02 2.563e-03 -16.80 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08876 on 1256 degrees of freedom
## Multiple R-squared: 0.2967, Adjusted R-squared: 0.295
## F-statistic: 176.6 on 3 and 1256 DF, p-value: < 2.2e-16# Todas las variables son significativas al 99% excepto ODA.ALL_diff y Regulatory QualityODA.PC
HDI = ODA.PC + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[3]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.22974 -0.05449 -0.00562 0.05266 0.56145
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.697e-01 7.684e-03 87.153 <2e-16 ***
## DT.ODA.ODAT.PC.ZS 6.806e-05 3.549e-05 1.918 0.0554 .
## GOV 6.627e-02 5.776e-03 11.475 <2e-16 ***
## SP.POP.GROW -4.327e-02 2.560e-03 -16.900 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08863 on 1256 degrees of freedom
## Multiple R-squared: 0.2988, Adjusted R-squared: 0.2971
## F-statistic: 178.4 on 3 and 1256 DF, p-value: < 2.2e-16# Todas las variables son significativas al 99% excepto Regulatory QualityODA.PC_diff
HDI = ODA.PC_diff + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[4]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23342 -0.05470 -0.00614 0.05312 0.57610
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.762e-01 6.906e-03 97.919 <2e-16 ***
## DT.ODA.ODAT.PC.ZS_diff -1.942e-05 6.719e-05 -0.289 0.773
## GOV 6.965e-02 5.508e-03 12.644 <2e-16 ***
## SP.POP.GROW -4.303e-02 2.561e-03 -16.801 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08876 on 1256 degrees of freedom
## Multiple R-squared: 0.2968, Adjusted R-squared: 0.2951
## F-statistic: 176.7 on 3 and 1256 DF, p-value: < 2.2e-16# Todas las variables son significativas al 99% excepto ODA.ALL_diff y Regulatory QualityHDI_diff
ODA.ALL
HDI_diff = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[5]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081219 -0.002722 0.000275 0.003123 0.075333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.860e-03 5.067e-04 9.591 <2e-16 ***
## DT.ODA.ALLD.CD -3.312e-13 1.713e-13 -1.933 0.0534 .
## GOV 7.315e-05 4.017e-04 0.182 0.8555
## SP.POP.GROW 3.494e-04 1.857e-04 1.881 0.0602 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.006406 on 1256 degrees of freedom
## Multiple R-squared: 0.005273, Adjusted R-squared: 0.002897
## F-statistic: 2.219 on 3 and 1256 DF, p-value: 0.08419# Todas las variables son significativas al 95% excepto ODA.ALL, Control of Corruption y Rule of LawODA.ALL_diff
HDI_diff = ODA.ALL_diff + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[6]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.080152 -0.002721 0.000216 0.003098 0.073931
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.667e-03 4.980e-04 9.373 <2e-16 ***
## DT.ODA.ALLD.CD_diff -8.802e-13 3.496e-13 -2.518 0.0119 *
## GOV 1.579e-04 3.973e-04 0.397 0.6912
## SP.POP.GROW 3.306e-04 1.848e-04 1.789 0.0739 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0064 on 1256 degrees of freedom
## Multiple R-squared: 0.007324, Adjusted R-squared: 0.004953
## F-statistic: 3.089 on 3 and 1256 DF, p-value: 0.0263# Todas las variables son significativas al 95% excepto Control of Corruption y Voice and AccountabilityODA.PC
HDI_diff = ODA.PC + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[7]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.078807 -0.002680 0.000203 0.002958 0.076519
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.834e-03 5.514e-04 10.581 < 2e-16 ***
## DT.ODA.ODAT.PC.ZS -1.204e-05 2.546e-06 -4.729 2.51e-06 ***
## GOV 7.817e-04 4.144e-04 1.886 0.0595 .
## SP.POP.GROW 3.549e-04 1.837e-04 1.932 0.0536 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.006359 on 1256 degrees of freedom
## Multiple R-squared: 0.01977, Adjusted R-squared: 0.01743
## F-statistic: 8.443 on 3 and 1256 DF, p-value: 1.481e-05# Todas las variables son significativas al 95% excepto Rule of Law y Voice and AccountabilityODA.PC_diff
HDI_diff = ODA.PC_diff + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[8]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.078419 -0.002772 0.000199 0.003081 0.072676
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.697e-03 4.966e-04 9.458 < 2e-16 ***
## DT.ODA.ODAT.PC.ZS_diff -1.747e-05 4.832e-06 -3.616 0.000312 ***
## GOV 1.843e-04 3.961e-04 0.465 0.641872
## SP.POP.GROW 3.212e-04 1.842e-04 1.744 0.081445 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.006383 on 1256 degrees of freedom
## Multiple R-squared: 0.01259, Adjusted R-squared: 0.01023
## F-statistic: 5.338 on 3 and 1256 DF, p-value: 0.001178# Todas las variables son significativas al 95% excepto Control of Corruption y Voice and AccountabilityGPD.PC
ODA.ALL
GPD.PC = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[9]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2076.9 -674.3 -222.5 453.4 4866.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.506e+03 7.360e+01 34.046 <2e-16 ***
## DT.ODA.ALLD.CD 3.534e-08 2.488e-08 1.420 0.156
## GOV 7.775e+02 5.835e+01 13.326 <2e-16 ***
## SP.POP.GROW -2.673e+02 2.698e+01 -9.908 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 930.5 on 1256 degrees of freedom
## Multiple R-squared: 0.207, Adjusted R-squared: 0.2051
## F-statistic: 109.3 on 3 and 1256 DF, p-value: < 2.2e-16# Todas las variables son significativas al 99% excepto ODA.ALL, Control of Corruption, Regulatory Quality y Rule of LawODA.ALL_diff
GPD.PC = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[10]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2093.1 -670.6 -221.3 444.9 4914.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.524e+03 7.246e+01 34.836 <2e-16 ***
## DT.ODA.ALLD.CD_diff -1.505e-08 5.086e-08 -0.296 0.767
## GOV 7.652e+02 5.781e+01 13.237 <2e-16 ***
## SP.POP.GROW -2.633e+02 2.689e+01 -9.792 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 931.2 on 1256 degrees of freedom
## Multiple R-squared: 0.2058, Adjusted R-squared: 0.2039
## F-statistic: 108.5 on 3 and 1256 DF, p-value: < 2.2e-16# Todas las variables son significativas al 99% excepto ODA.ALL_diff, Control of Corruption, Regulatory Quality y Rule of LawODA.PC
GPD.PC = ODA.PC + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[11]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1976.9 -650.5 -220.3 430.6 4496.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2310.9576 79.5755 29.04 < 2e-16 ***
## DT.ODA.ODAT.PC.ZS 2.2345 0.3675 6.08 1.59e-09 ***
## GOV 654.8088 59.8090 10.95 < 2e-16 ***
## SP.POP.GROW -271.0975 26.5131 -10.22 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 917.8 on 1256 degrees of freedom
## Multiple R-squared: 0.2285, Adjusted R-squared: 0.2266
## F-statistic: 124 on 3 and 1256 DF, p-value: < 2.2e-16# Todas las variables son significativas al 99% excepto Rule of LawODA.PC_diff
GPD.PC = ODA.PC_diff + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[12]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2092.4 -667.9 -221.6 449.1 4914.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2524.5201 72.4553 34.842 <2e-16 ***
## DT.ODA.ODAT.PC.ZS_diff -0.1304 0.7049 -0.185 0.853
## GOV 765.6854 57.7923 13.249 <2e-16 ***
## SP.POP.GROW -263.4905 26.8720 -9.805 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 931.2 on 1256 degrees of freedom
## Multiple R-squared: 0.2058, Adjusted R-squared: 0.2039
## F-statistic: 108.5 on 3 and 1256 DF, p-value: < 2.2e-16# Todas las variables son significativas al 99% excepto ODA.ALL_diff, Control of Corruption, Regulatory Quality y Rule of LawGPD.PC_diff
ODA.ALL
GPD.PC_diff = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[13]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1823.28 -54.66 -8.44 57.85 998.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.171e+02 1.429e+01 8.197 6.01e-16 ***
## DT.ODA.ALLD.CD 3.018e-09 4.830e-09 0.625 0.53222
## GOV 2.807e+01 1.133e+01 2.479 0.01331 *
## SP.POP.GROW -1.710e+01 5.237e+00 -3.265 0.00113 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 180.6 on 1256 degrees of freedom
## Multiple R-squared: 0.01551, Adjusted R-squared: 0.01316
## F-statistic: 6.596 on 3 and 1256 DF, p-value: 0.0002014# Todas las variables son significativas al 95% excepto ODA.ALL, Control of Corruption, Regulatory Quality y Voice and AccountabilityODA.ALL_diff
GPD.PC_diff = ODA.ALL + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[14]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1825.99 -54.34 -9.01 57.16 995.05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.187e+02 1.406e+01 8.447 < 2e-16 ***
## DT.ODA.ALLD.CD_diff 1.283e-09 9.867e-09 0.130 0.89653
## GOV 2.710e+01 1.121e+01 2.417 0.01581 *
## SP.POP.GROW -1.680e+01 5.216e+00 -3.221 0.00131 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 180.6 on 1256 degrees of freedom
## Multiple R-squared: 0.01522, Adjusted R-squared: 0.01287
## F-statistic: 6.469 on 3 and 1256 DF, p-value: 0.0002407# Todas las variables son significativas al 95% excepto ODA.ALL_diff, Control of Corruption, Regulatory Quality y Voice and AccountabilityODA.PC
GPD.PC_diff = ODA.PC + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[15]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1828.11 -54.96 -9.29 58.56 992.60
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 123.36451 15.65873 7.878 7.14e-15 ***
## DT.ODA.ODAT.PC.ZS -0.04877 0.07232 -0.674 0.50021
## GOV 29.48222 11.76912 2.505 0.01237 *
## SP.POP.GROW -16.61104 5.21721 -3.184 0.00149 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 180.6 on 1256 degrees of freedom
## Multiple R-squared: 0.01556, Adjusted R-squared: 0.01321
## F-statistic: 6.618 on 3 and 1256 DF, p-value: 0.0001953# Todas las variables son significativas al 95% excepto ODA.PC, Control of Corruption, Political Stability, Regulatory Quality y Voice and AccountabilityODA.PC_diff
GPD.PC_diff = ODA.PC_diff + CC + GE + PV + RQ + RL + VA + POP.GROW
summary(models[[keys[16]]])##
## Call:
## lm(formula = f, data = datos_model)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1826.13 -54.92 -9.07 56.70 995.26
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 118.64090 14.05293 8.442 < 2e-16 ***
## DT.ODA.ODAT.PC.ZS_diff 0.08499 0.13672 0.622 0.53430
## GOV 27.06141 11.20899 2.414 0.01591 *
## SP.POP.GROW -16.80985 5.21191 -3.225 0.00129 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 180.6 on 1256 degrees of freedom
## Multiple R-squared: 0.01551, Adjusted R-squared: 0.01316
## F-statistic: 6.595 on 3 and 1256 DF, p-value: 0.0002018# Todas las variables son significativas al 95% excepto ODA.PC_diff, Control of Corruption, Regulatory Quality y Voice and AccountabilityModelos Efectos fijos
HDI ~ ODA.ALL
plm(hdi ~ DT.ODA.ALLD.CD + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW, data=datos_model,
index = c("iso2c", "year"), model = "within")##
## Model Formula: hdi ~ DT.ODA.ALLD.CD + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST +
## VA.EST + SP.POP.GROW
##
## Coefficients:
## DT.ODA.ALLD.CD CC.EST GE.EST PV.EST RQ.EST
## 2.0890e-11 -8.3315e-03 7.5391e-03 -4.4617e-03 2.0938e-02
## RL.EST VA.EST SP.POP.GROW
## 3.2560e-02 6.9264e-03 -5.5903e-03# summary(lm(hdi ~ DT.ODA.ALLD.CD + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW + iso2c, data=datos_model))
# Corruption Control, Government Effectiveness, Political Stability y Voice and Accountability no son significativasHDI ~ ODA.PC
plm(hdi ~ DT.ODA.ODAT.PC.ZS + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW, data=datos_model,
index = c("iso2c", "year"), model = "within")##
## Model Formula: hdi ~ DT.ODA.ODAT.PC.ZS + CC.EST + GE.EST + PV.EST + RQ.EST +
## RL.EST + VA.EST + SP.POP.GROW
##
## Coefficients:
## DT.ODA.ODAT.PC.ZS CC.EST GE.EST PV.EST
## 0.0001890 -0.0090492 -0.0012169 -0.0066846
## RQ.EST RL.EST VA.EST SP.POP.GROW
## 0.0225845 0.0433849 0.0048044 -0.0089386#summary(lm(hdi ~ DT.ODA.ODAT.PC.ZS + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW + iso2c, data=datos_model))
# Corruption Control, Government Effectiveness y Voice and Accountability no son significativasGDP.PC ~ ODA.ALL
plm(NY.GDP.PCAP.CD ~ DT.ODA.ALLD.CD + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW, data=datos_model,
index = c("iso2c", "year"), model = "within")##
## Model Formula: NY.GDP.PCAP.CD ~ DT.ODA.ALLD.CD + CC.EST + GE.EST + PV.EST +
## RQ.EST + RL.EST + VA.EST + SP.POP.GROW
##
## Coefficients:
## DT.ODA.ALLD.CD CC.EST GE.EST PV.EST RQ.EST
## 2.6336e-07 -2.1804e+01 4.6280e+02 1.9040e+02 -5.2602e+01
## RL.EST VA.EST SP.POP.GROW
## -1.6883e+02 -2.6173e+01 -3.7759e+01#summary(lm(NY.GDP.PCAP.CD ~ DT.ODA.ALLD.CD + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW + iso2c, data=datos_model))
# Intercepto, Corruption Control, Regulatory Quality, Rule of Law, Voice and Accountability y Population Growth no son significativasGDP.PC ~ ODA.PC
plm(NY.GDP.PCAP.CD ~ DT.ODA.ODAT.PC.ZS + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW, data=datos_model,
index = c("iso2c", "year"), model = "within")##
## Model Formula: NY.GDP.PCAP.CD ~ DT.ODA.ODAT.PC.ZS + CC.EST + GE.EST + PV.EST +
## RQ.EST + RL.EST + VA.EST + SP.POP.GROW
##
## Coefficients:
## DT.ODA.ODAT.PC.ZS CC.EST GE.EST PV.EST
## 3.6490 -82.8703 370.2174 156.9927
## RQ.EST RL.EST VA.EST SP.POP.GROW
## -13.5138 -2.0353 -66.6104 -85.1696#summary(lm(NY.GDP.PCAP.CD ~ DT.ODA.ODAT.PC.ZS + CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST + SP.POP.GROW + iso2c, data=datos_model))
# Corruption Control, Regulatory Quality, Rule of Law y Voice and Accountability no son significativas